Ensemble Regression (Random Forest, Gradient Boosting)

Fits ensemble regression models for maximum predictive accuracy. Random Forest for variance reduction and robustness, Gradient Boosting for highest performance. Returns predictions and feature importances. [Tier: ENTERPRISE, Credits: 10]

POST /quantlib/ml/regression/ensemble

Ensemble Regression (Random Forest, Gradient Boosting)

cURL

curl --request POST \
  --url https://api.fincept.in/quantlib/ml/regression/ensemble \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "X": [
    [
      1.2,
      0.5,
      3.1
    ],
    [
      2.1,
      1.3,
      2.5
    ],
    [
      0.8,
      0.9,
      4.2
    ]
  ],
  "y": [
    150000,
    235000,
    185000
  ],
  "method": "gradient_boosting",
  "n_estimators": 100,
  "max_depth": 5,
  "predict_X": [
    [
      1.5,
      0.7,
      3.3
    ]
  ]
}
'

200

{
  "success": true,
  "data": {
    "predictions": [
      197500
    ]
  }
}

Authorizations

X-API-Key · string · header · required

API key for authentication. Get your key at https://api.fincept.in/auth/register

Body

application/json

X · number[][] · required

Feature matrix

Example:

[
  [1.2, 0.5, 3.1],
  [2.1, 1.3, 2.5],
  [0.8, 0.9, 4.2]
]

y · number[] · required

Continuous target values

Example:

[150000, 235000, 185000]

method · enum<string> · default: random_forest

Ensemble method

Available options: random_forest, gradient_boosting

Example:

"gradient_boosting"

n_estimators · integer · default: 100

Number of trees

Example:

100

max_depth · integer · default: 5

Maximum tree depth

Example:

5

predict_X · number[][]

Optional feature matrix for prediction

Example:

[[1.5, 0.7, 3.3]]

Response

200

application/json

Ensemble regression results

success · boolean

Example:

true

data · object

Show child attributes